Distribution of genetic variation underlying adult ...

Distribution of genetic variation underlying adult

migration timing in steelhead of the Columbia River basin

E.E. Collins, J.S. Hargrove, T.A. Delomas, S.R. Narum

2020 Ecology & Evolution

Background

Fish migrations• temporal and spatial

availability of resources

• cultural, economic, and ecological resource

ManagementCauses of decline:

• overharvest, habitat degradation, hydroelectric dams, climate change, other anthropogenic development

Solutions:• Identify evolutionarily significant units (ESU)• maintain phenotypic and genetic variation of distinct populations, such

as migration timing

Image credit: Robin Ade

Background• Bimodal migration timing in steelhead

• Early / summer-run mature in streams and late / winter-run mature in ocean, both spawn at the same time

Adapted from: Hess et al. 2016

Early /

Late /

• Migration timing has genetic basis and is heritable

Parents

Offspring

Background

Equilibrium

Background• Genetic diversity and conservation

Genetically Diverse Parents

Higher stream temperatures

Genetically UniformParents

Offspring SurvivalBetter

Offspring Survival Worse

Higher stream temperatures

Background

• Neutral vs. adaptive genetic markers

• Neutral genetic markers differentiate between genetic lineages

• coastal or inland

• distinct populations

• Adaptive markers can differentiate between phenotypic traits

• migration-timing

• sex

• thermal-tolerances

• age

• etc.

• Within Populations

• Low variation at neutral genetic markers

• Greater variation in adaptive markers

Background

• Previous studies

Adapted from: Hess et al. 2016

Adapted from: Micheletti et al. 2018

Adapted from: Prince et al. 2018

MethodsSample Collection

• Inland and coastal sampled

• 1996-2018

• Electrofishing, smolt traps, weirs

• Non-lethal fin tissue

Analyses• Neutral marker PCA• Migration timing genotype proportions were assessed across all collection

locations• Linkage disequilibrium (LD) within the adaptive markers to identify haplotype

blocks• Redundancy analyses (RDA) were conducted for all Columbia River basin

collections to model the degree to which the variation in environmental variables explained the variation in allele frequencies of adaptive markers

Coastal Inland

Adapted from: Collins et al. 2020

Methods• Migration timing is heritable – genomic region of

major effect

• greb1L – initial SNPs identified with RAD-seq methods

• More SNPs identified on greb1L, intergenic, rock1with Pool-seq methods


Results

• 9,471 steelhead from 113 populations

• 226 neutral markers


Results


• 226 neutral markers


Coastal Inland

Coastal

Inland

Results


Results

• Late returning (blue) steelhead are the most common

• Early returning (red) are rarer: Only 9 out of 113 (8%) populations had higher frequency for early migration

• More genetic diversity in coastal populations than inland

• Corresponds with observations that early returning fish have experienced the greatest decline over time

• Concerns over loss of variation over time

Late

Early


Results

• Analysis to find environmental factors that drive genetic variation associated with migration timing:• Migration distance• Temperature• Precipitation

• Significant relationships between environmental variables and genetics suggest that these may be environmental drivers leading to local adaptation among populations.


Conclusions• Neutral marker

analyses of population structure, supported and improved upon previous findings


Conclusions• We determined

linkage blocks for 13 adaptivemarkers associated with migration timing


Conclusions• Different heterozygote haplotypes were found to be

predominant in coastal versus inland lineages.


Early

Late

Conclusions

• Candidate adaptive marker variation revealed the importance of temperature and precipitation


ConclusionsMonitor Genetic Diversity

• Maintain or improve underlying genetic variation for native fish species in Columbia River to provide broader life history diversity for populations to endure stochastic environments

• Combination of genetic markers

• Monitor distinct populations

• Specific migration related traits (timing, age, sex, thermal-tolerance)

Late

Early

Coastal

Inland

Acknowledgments

• Sample Collection Crews

Nez Perce Tribe, Umatilla Tribes, Warm Springs Tribes, Yakama Nation, Idaho Fish and Game, Oregon Fish and Wildlife, Washington Fish and Wildlife

• Lab work

Janae Cole, Joe Gasper, Amber Gonzales, Stephanie Harmon, Travis Jacobson, Vanessa Jacobson, Lori Maxwell, Megan Moore, Becca Sanders, Jeff Stephenson

• Co-authors

John Hargrove, Thomas Delomas, Shawn Narum

• Funding

Bonneville Power Administration

Questions?

Filtering of genotype data

• Quality threshold of >90% loci successfully genotyped

• And had an estimated <0.5% genotyping error based on replicate genotyping

Results


Results


Coastal Inland

Coastal MAF averagesInland MAF averages

0

10

20

30

40

50

60

70

80

Ave

rage

Gen

oty

pe

Fre

qu

enci

es (

%)

Marker 9

Markers 2,3,6

Markers 2,3,6,9

Markers 2-7

Markers 8-12

Markers 2-12

Premature Heterozygote Mature

Results


• Markers 2,3,6

Results


• Marker 9 alone

Late Late

Early

Early

Results

• Redundancy analyses run separately for each genetic lineage

• Environmental factors assessed:• Migration distance

• Temperature

• Precipitation


Diversity of inland lineage at adaptivemarkers

Late

Early


Order

IDSNP Chr Position Gene Forward primer Reverse primer Probe Orientation

1 Omy28_11607954 28 11607954 greb1L

TGACACTGATCACA

ATGGTGAAAT

TAAACTGGAAGGAG

AGAGCAAAAT TGTGGGCTGC[A/G]AACATACTCA +

2 Omy_RAD52458-17 28 11609794 greb1L

ACGTGTCCCTGAGG

ATGGTA

AGCTCTAGGTCTGG

GTCCTG ATGGCCC[C/A][CT]AAGAACCC -

3 Omy_GREB1_05 28 11618027 greb1L

TGGGCAGATATGG

AAGAACGG

ACCTTCTAAATGGCC

TCTGTGT CGGTGGCTC[T/G]C +

4 Omy28_11625241 28 11625241 greb1L

CAACATTTAGGGAG

AGGTTGCTAT

ATCATCAAGTTTGCC

TACGACAC CCTCCTCCCT[A/G]TGGTTGTCTC +

5 Omy28_11632591 28 11632591 greb1L

GTAGAGGCCAAAG

GCTTGAG

TGCTCTTATTACCTTC

CAGACTCC TGAGAA[G/A]AACACAGAGG +

6 Omy_GREB1_09 28 11641623 greb1L

CCAGTGGCAACCTC

AGGTAG

GACTCCAGTCACCCA

AGTCA TCAA[T/G]GGAGA +

7 Omy28_11658853 28 11658853 intergenic

CAACATATGACCAC

TCGAAAACTC

ATTAATCACACCGTG

AGACTCCTC TGGTACAGAC[A/C]CGCACTAGCA +

8 Omy28_11667578 28 11667578 intergenic

ACAGTAAACCCATT

CAGGCATAGT

TTATCCTCTCAATCC

ACATCAAGA GTATTGATCC[T/C]GTGGGAGACA +

9 Omy_RAD47080-54 28 11667915 intergenic

TCAAAACCTGCAGG

ACTTGGA

TGGTTATATCTACAG

TACAGTTCGT TGCAAG[A/G]CTTAAAACGA +

10 Omy28_11671116 28 11671116 intergenic

AATTTCCCCAAATTT

GAAACTCTT

GTGTACATTGTCAGG

CAGAAACAT CTGGTGAGAA[C/T]AGGAATTACC +

11 Omy28_11676622 28 11676622 intergenic

CGAATGCACTGTAG

CTCATTCTAA

GCAGTAGAATGTCTC

GCAAATACA ACATGTCATT[T/G]ATTGTTATCT +

12 Omy28_11683204 28 11683204 intergenic

CAAGAAAGAAACA

GATGTTGTCCA

TTGTGACTCAAATCT

GCAACCTAT ATGTAAAAAA[G/T]GGCAGAAAA +

13 Omy28_11773194 28 11773194 rock1

AGTTTGACACCCCT

GTACTAGAGC

GTCTAACAAGCTCTG

GGTGATTTA GCAATTTTTT[T/A]AAATTACCGC +

Results


• 13 adaptive markers


Late migration associated alleles

Mix of alleles associated with both types of migration

Early migration associated alleles

Distribution of genetic variation underlying adult ...

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